U.S. patent application number 12/441197 was filed with the patent office on 2009-12-24 for biofluid metabolite profiling as a tool for early prediction of autoimmunity and type 1 diabetes risk.
This patent application is currently assigned to ZORA BIOSCIENCES OY. Invention is credited to Matej Oresic, Tuulikki Seppanen-Laakso, Olli Simell, Marko Sysi-Aho.
Application Number | 20090318392 12/441197 |
Document ID | / |
Family ID | 38900749 |
Filed Date | 2009-12-24 |
United States Patent
Application |
20090318392 |
Kind Code |
A1 |
Oresic; Matej ; et
al. |
December 24, 2009 |
BIOFLUID METABOLITE PROFILING AS A TOOL FOR EARLY PREDICTION OF
AUTOIMMUNITY AND TYPE 1 DIABETES RISK
Abstract
The invention concerns a method for diagnosing a child's
susceptibility for developing type 1 diabetes by using a serum
metabolite as biomarker. The invention concerns also a method for
prevention of the onset of type 1 diabetes in a child.
Inventors: |
Oresic; Matej; (Espoo,
FI) ; Simell; Olli; (Raisio, FI) ;
Seppanen-Laakso; Tuulikki; (Espoo, FI) ; Sysi-Aho;
Marko; (Helsinki, FI) |
Correspondence
Address: |
YOUNG & THOMPSON
209 Madison Street, Suite 500
Alexandria
VA
22314
US
|
Assignee: |
ZORA BIOSCIENCES OY
Espoo
FI
|
Family ID: |
38900749 |
Appl. No.: |
12/441197 |
Filed: |
September 10, 2007 |
PCT Filed: |
September 10, 2007 |
PCT NO: |
PCT/FI2007/000227 |
371 Date: |
June 5, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60844357 |
Sep 14, 2006 |
|
|
|
Current U.S.
Class: |
514/114 ;
436/131; 436/71; 514/642 |
Current CPC
Class: |
A61P 3/10 20180101; Y10T
436/203332 20150115; G01N 33/6893 20130101; G01N 33/38 20130101;
G01N 33/92 20130101; G01N 2405/04 20130101; G01N 2800/042
20130101 |
Class at
Publication: |
514/114 ; 436/71;
436/131; 514/642 |
International
Class: |
A61K 31/66 20060101
A61K031/66; G01N 33/92 20060101 G01N033/92; A61K 31/14 20060101
A61K031/14 |
Claims
1-21. (canceled)
22. A method for diagnosing a child's susceptibility for developing
type I diabetes, wherein said method comprises i) determining the
concentration of at least one serum metabolite in the child to be
diagnosed, ii) comparing the serum concentration of said metabolite
to the serum concentration of the same metabolite in a control
group of healthy children, iii) using a concentration difference
between the child to be diagnosed and the control group as a
biomarker indicative of the child's susceptibility for developing
type I diabetes.
23. The method according to claim 22, wherein the age of the child
to be diagnosed is the same or approximately the same as that of
the control group.
24. The method according to claim 22, wherein the biomarker is a
metabolite protecting against oxidative stress and/or inflammation,
and a decreased concentration thereof in the child to be diagnosed,
compared to the control group of healthy children, is indicative of
the child's susceptibility for developing type I diabetes.
25. The method according to claim 24 wherein the biomarker is a
phospholipid, an acid or a derivative thereof, a ketone, or an
alcohol.
26. The method according to claim 25, wherein the biomarker is
total phospholipids.
27. The method according to claim 25, wherein the biomarker is one
or more ester linked phosphocholines.
28. The method according to claim 25, wherein the biomarker is
total ester linked phosphocholines.
29. The method according to claim 22, wherein the biomarker is
determined in newborn children.
30. The method according to claim 28, wherein the child is a
newborn child and child's level of total ester linked
phosphocholines being about 80% or less of the mean level for the
control group is used as indicative of the child's susceptibility
for developing type I diabetes.
31. The method according to claim 25, wherein the biomarker is one
or more ether linked phosphocholine, or an ethanolamine
plasmalogen.
32. The method according to claim 31, wherein the ether linked
phosphatidylcholine is selected from the group consisting of GPCho
(36:2e), GPCho (38:1e), GPCho (38:5e), GPCho (40:4e), CPCho
(O-18:1/16:0), CPCho (O-18:1/16:1), CPCho (O-16:0/20:4), CPCho
(O-18:1/20:4) and CPCho (O-18:0/18:2), and the determination
thereof is made at a child age ranging from newborn to six years'
age.
33. The method according to claim 31, wherein the ethanolamine
plasmalogen is GPEtn (O-18:1(1Z)/20:4), and the determination
thereof is made at a child age ranging from newborn to six years'
age.
34. The method according to claim 25, wherein the biomarker is
selected from a group consisting of tryptophan, ribitol,
pentanedioic acid, glycine, eicosanoic acid,
1,2,3-propanetricarboxylic acid, myristoleic acid, mannitol,
creatinine, butanedioic acid, heptanoic acid and 2-ketoglutaric
acid methoxime.
35. The method according to claim 22, wherein the determination of
the serum metabolite is made at several ages of the child and the
result is compared to control groups of the same age as the child
to be diagnosed.
36. The method according to claim 35, wherein several serum
metabolites are determined for the child to be diagnosed, and the
levels are compared to the levels of said metabolites for control
groups.
37. The method according to claim 22, wherein the genetic risk for
development of type 1 diabetes and/or the emergence of autoimmunity
also is determined.
38. The method according to claim 37, wherein the genetic risk for
development of type 1 diabetes and/or the emergence of autoimmunity
are followed by metabolite markers as progressive disease
susceptibility detection.
39. The method according to claim 37, wherein the emergence of
autoantibody markers in combination with the decreased ether linked
phosphocholine levels are determined to identify individuals at
higher risk of developing type 1 diabetes.
40. A method for prevention of the onset of type 1 diabetes in a
child, said child having been diagnosed according to claim 22, as
susceptible for developing type I diabetes, said method comprising
subjecting said child to one or more measures preventing the onset
of diabetes.
41. The method according to claim 40, wherein the preventing
measure is a nutritional intervention, an antioxidant therapy, or a
stimulation of the biochemical synthesis of choline plasmalogens in
the child, or any combination of said measures.
42. The method according to claim 41, wherein the nutritional
intervention is a choline supplement in the mother's diet, a
choline supplement in the child's diet or a choline plasmalogen
supplement in the child's diet.
Description
FIELD OF THE INVENTION
[0001] This invention relates to a method for early diagnosing of a
child's susceptibility for developing type 1 diabetes. Furthermore,
the invention also relates to a method for the prevention of type 1
diabetes in a child diagnosed as susceptible for developing type 1
diabetes.
BACKGROUND OF THE INVENTION
[0002] The publications and other materials used herein to
illuminate the background of the invention, and in particular,
cases to provide additional details respecting the practice, are
incorporated by reference.
[0003] Type 1 diabetes is an autoimmune disease, in which the
body's own immune system attacks the .beta. cells in the islets of
Langerhans of the panceras, destroying them or damaging them
sufficiently to reduce or eliminate insulin production.
[0004] During the past 10-50 years the incidence of type 1
diabetes, the most common metabolic-endocrine disease of children,
has increased for unknown reasons in almost all western countries.
Development of .beta. cell-specific autoantibodies, believed to
occur together with selective functional impairment and ultimate
destruction of the insulin-producing .beta. cells in the pancreatic
islets of Langerhans, commonly precedes the onset of overt type 1
diabetes by months to years. The unknown initiators of autoimmunity
and the poorly understood mechanisms supporting progression towards
.beta. cell failure not only hinder estimation of the absolute
disease risk and time of onset of the disease in genetically
susceptible individuals, but also hamper discovery of effective
prevention.
OBJECTS AND SUMMARY OF THE INVENTION
[0005] An object of the present invention is to provide a method
for early diagnosis of a child's susceptibility for developing type
1 diabetes.
[0006] Particularly, the object is to provide a method for
diagnosing the child's risk of developing type 1 diabetes in months
or years before the clinical onset of the disease, preferably even
before the emergency of autoantibodies in the child's serum. A
particular object is to provide a method for diagnosing even a
newborn child's risk of developing type 1 diabetes at a later
stage.
[0007] Furthermore, one object of this invention is to provide
means for prevention of the onset of type 1 diabetes in a child
diagnosed as susceptible for developing type 1 diabetes.
[0008] To overcome the obstacles relating to established methods,
we launched Type 1 Diabetes Prediction and Prevention study (DIPP),
a large birth cohort study, in Finland in 1994.sup.1. The type 1
diabetes risk- and protection-associated HLA-alleles were first
analyzed in cord blood of newborns after parental informed consent.
Children carrying increased genetic risk were then frequently
examined to discover when diabetes-associated autoantibodies
emerged, or clinical diabetes developed. During the 11.5 years of
the study, over 100,000 newborns have been screened, over 450 have
developed multiple autoantibodies indicating markedly elevated
disease risk, and 138 of those remaining in the study have so far
progressed to clinical diabetes, providing a unique series of
samples for studies of disease pathogenesis and prediction.
[0009] Serum patterns of metabolites at least to some extent
reflect homeostasis of the system, and changes in specific
metabolite groups may system responses to environmental or genetic
alterations or interventions.sup.2. Metabolomics platform,
applicable to all species, follows a time-response, and has
capability for high sample throughput. The metabolic phenotype is
also affected by environmental factors such as nutrition and gut
microbiota.sup.3,4, which are of particular relevance to complex
diseases such as type 1 diabetes, believed to be affected both by
genetic factors and the environment.sup.5. With today's analytical
and information technologies for handling of large volumes of data,
the metabolomics approach has become increasingly feasible.
Therefore, metabolomics may provide powerful tools for
characterization of e.g. complex phenotypes and biomarkers for
selected physiological and pathological responses.sup.6,7.
[0010] Thus, in its broadest aspect, this invention relates to a
method for diagnosing a child's susceptibility for developing type
I diabetes, wherein said method comprises the steps of
[0011] i) determining the concentration of at least one serum
metabolite in the child to be diagnosed,
[0012] ii) comparing the serum concentration of said metabolite to
the serum concentration of the same metabolite in a control group
of healthy children, and
[0013] iii) using a concentration difference between the child to
be diagnosed and the control group as a biomarker indicative of the
child's susceptibility for developing type I diabetes.
[0014] In another aspect, this invention relates to a method for
prevention of the onset of type 1 diabetes in a child, said child
having been diagnosed according to this invention, as susceptible
for developing type I diabetes, said method comprising subjecting
said child one or more measures preventing the onset of
diabetes.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1. Design of the DIPP study and the sample selection
for metabolomics. (a) DIPP study design. (b) Autoantibody profile
of a child who developed diabetes at age of 9 years. (c) Samples
included in the study. (d) Design of analysis and the main
questions addressed in this paper.
[0016] FIG. 2. Two-dimensional Sammon's mapping of all samples in
the Oulu batch. Total 518 samples included, with 186 identified
lipids as variables. Four different potential confounding factors
are visualized following the mapping. (a) Individual ID, (b)
gender, (c) age, and (d) sample age.
[0017] FIG. 3. Profiles of selected ether linked phosphocholine
species from DIPP Turku batch. (a) Longitudinal profiles of
GPCho(36:2e). (b) Longitudinal profiles of GPCho(40:4e). (c)
GPCho(36:2e) levels at age of 1 year. Only one sample per
individual included, nearest to 1 year of age. (d) GPCho(36:2e)
levels at age of 3 years. (e) GPCho(36:2e) levels at age of 6
years.
[0018] FIG. 4. Cord blood lipid profiles. (a) The score plot
reveals differences in lipid profiles between the progressors and
majority of non-progressors at birth. (b) The loadings reveal the
differences are attributed to phospholipids. (c) The ether linked
phosphocholine GPCho(36:2e) is not different between progressors
and non-progressors. (d) Total phosphocholine level, calculated as
a sum of concentration of all ester linked glycerophosphocholine
molecular species.
[0019] FIG. 5. Lipid profiles within the 6 month interval before
and after seroconversion. (a) Changes in lipid profiles are
detected using PLS/DA analysis. (b) Lysophosphatidylcholine species
LysoGPCho(18:0), known to be associated with inflammation, is
upregulated in progressors prior to seroconverion, i.e. emergence
of first autoantibodies. (c) Ether linked phosphoethanolamine
species GPEth(38:1e) is upregulated after seroconversion in
progressors.
[0020] FIG. 6. Summary of findings in context of Type 1 Diabetes
pathogenesis.
[0021] FIG. 7. Pathway showing the synthesis choline plasmalogens
from DHAP.
[0022] FIG. 8. Changes in ether phosphatidylcholine levels in
progressors between the ages of 1.5 and 5 years Only one sample per
individual, drawn closest to the age of 1.5 or 5 years is included.
(Panel A). The exact fatty acid position (i.e., sn1 vs. sn2) and
double bond locations were not confirmed. Panel B shows box plot of
GPCho(O-18:1/16:0) concentrations for progressors and
non-progressors. The box contains the middle 50% of the data. The
upper edge (hinge) of the box indicates the 75th percentile of the
data, and the lower hinge indicates the 25th percentile.
Illustrative longitudinal profiles for two progressors and two
non-progressors (Panel C). Panel D lists lysophosphatidylcholine
level changes between progressors and non-progressors within a
9-month period preceding seroconversion and soon thereafter. The
non-progressor selected time points were closest to those of
matched progressors.
[0023] FIG. 9. Early age differences between progressors and
non-progressors for the ether phosphatidylcholine GPCho(O-18/18:2).
Levels for children with ages between 315 and 405 days (1 year) are
shown in Panel A, with ages between 630 and 810 days (2 years) in
Panel B, and with ages between 1980 and 2340 days (6 years) in
Panel C. Only one sample per individual included, obtained nearest
to the indicated age. Panel D shows the longitudinal profiles of
GPCho(O-18:0/18:2) for subjects from batch 1.
[0024] FIG. 10. Early age differences between progressors and
non-progressors for the ethanolamine plasmalogen
GPEtn(O-18:1(1Z)/20:4). The plasmalogen levels are shown for
children with ages between 315 and 405 days (Panel A) and with ages
between 1980 and 2340 days (Panel B).
DETAILED DESCRIPTION OF THE INVENTION
[0025] We hypothesized that abnormalities in serum metabolite
profiles might precede onset of autoimmunity revealing pathways
essential for type 1 diabetes development. We tested this
hypothesis by screening during 11.5 years genetic diabetes risk in
over 100,000 consecutive newborns and enrolling the children with
genetic diabetes risk to tight follow-up. From over 8500 children
continuing in the study, over 450 have produced multiple types of
autoantibodies, and 138 have progressed to overt type 1 diabetes.
The metabolite profiles in 1039 serum samples collected at 3- to
12-month intervals between birth and diabetes onset from a cohort
of 47 children were compared with the profiles of 60 children who
remained healthy and autoantibody negative. We observed marked
differences in metabolite patterns in cord blood and later samples
between progressors and permanently autoantibody-negative children.
Metabolites protecting against oxidative stress and those related
to inflammation differed strongly between the cases and the
controls markedly earlier than the children seroconverted to
autoantibody positivity. The findings imply that metabolomics can
effectively be applied to screening of diabetes risk in infancy and
early childhood, and suggest that poor protection against oxidative
damage and inflammation plays important role in the pathogenesis of
diabetes.
Preferred Embodiments
[0026] The serum metabolite to be used as biomarker is preferably a
metabolite protecting against oxidative stress and/or inflammation.
In this case, a decreased concentration thereof in the child to be
diagnosed, compared to the control group of healthy children, is
indicative of the child's susceptibility for developing type I
diabetes.
[0027] The wording "decreased concentration" shall be understood to
mean that the level of the biomarker in the child belonging to the
risk group may be up to 80% of the level of the same biomarker in
healthy controls. However, typically the level in the risk group is
at highest 75%, more typically 65 . . . 50% of the level in the
controls.
[0028] In one preferred embodiment, the biomarker is total
phospholipids, one or more ester linked phosphocholines, or total
ester linked phosphocholines. In all these cases, the biomarker is
preferably determined already in newborn children, for example by
cord blood analyses. In a particularly preferred embodiment, the
child is a newborn child and child's level of total ester linked
phosphocholines being about 80% or less of the mean level for the
control group is used as indicative of the child's susceptibility
for developing type I diabetes.
[0029] In another preferred embodiment, the biomarker is one or
more ether linked phosphocholines, such as (but not restricted to)
GPCho (36:2e), GPCho (38:1e), GPCho (38:5e), GPCho (40:4e), CPCho
(O-18:1/16:0), CPCho (O-18:1/16:1), CPCho (O-16:0/20:4), CPCho
(O-18:1/20:4) or CPCho (O-18:0/18:2). Ether linked phosphocholines
can be determined at a child age ranging from newborn to six years'
age, preferably in the age of 1-2 years.
[0030] In a further preferred embodiment, the biomarker is an
ethanolamine plasmalogen such as GPEtn (O-18:1(1Z)/20:4). This
biomarker can be determined at a child age ranging from newborn to
six years' age.
[0031] In a still further embodiment, the biomarker is an acid or a
derivative thereof, a ketone, or an alcohol. As non-limiting
examples of biomarkers in this group can be mentioned tryptophan,
ribitol, pentanedioic acid, glycine, eicosanoic acid,
1,2,3-propanetricarboxylic acid, myristoleic acid, mannitol,
creatinine, butanedioic acid, heptanoic acid, and 2-ketoglutaric
acid methoxime. Out of these compounds, tryptophan, ribitol,
pentanedioic acid, 1,2,3-propanetricarboxylic acid, creatinine enol
and butanedioic acid are believed to be the most preferable
ones.
[0032] The determination of the serum metabolite can be followed up
at several ages of the child and the result is compared to control
groups of the same age as the child to be diagnosed.
[0033] Also, several serum metabolites can be determined for the
child to be diagnosed, and the levels are compared to the levels of
said metabolites for control groups. All or some of such
metabolites can be monitored over time.
[0034] The aforementioned monitoring of one or more serum
metabolites can be combined with determination of genetic risk for
development of type 1 diabetes and/or monitoring of emergence of
autoimmunity in the child.
[0035] Preferably, the genetic risk for development of type 1
diabetes and/or the emergence of autoimmunity are followed by
metabolite markers as progressive disease susceptibility
detection.
[0036] Most preferably, the emergence of autoantibody markers in
combination with the decreased ether linked phosphocholine levels
are determined to identify individuals at higher risk of developing
type 1 diabetes.
[0037] Once diagnosed at an early stage as belonging to the risk
group, the onset of type 1 diabetes in the child can be prevented
in many various ways. The preventing measure can be, for example, a
nutritional intervention, an antioxidant therapy, or a stimulation
of the biochemical synthesis of choline plasmalogens in the child,
or any combination thereof.
[0038] As potential preventive measures for type 1 diabetes
following from our results, are the use of nutritional
interventions that are known to be safe, for example: [0039]
Choline supplements for mothers, particularly if any of parents
carrying the risk genotype. [0040] Choline supplements for children
after birth if phosphocholine levels found low. [0041] Choline
plasmalogen supplements for children if phosphocholine levels found
low at birth or low ether linked phosphocholine found low later
on.
[0042] As a potential drug therapy, the antioxidant therapy is an
option. As an alternative, stimulating the synthesis of endogenous
antioxidants choline plasmalogens, found down-regulated in this
invention, is one possible option. The pathway is shown in FIG.
7.
[0043] The invention will be illuminated by the following
non-restrictive Experimental Section.
[0044] Experimental Section
[0045] We applied high-throughput metabolomics technologies to
analyze the serum sample series collected from our study children
between birth and development of overt diabetes (the progressors),
and compared the findings to those obtained by studying sample
series from control children matched for age, gender, genetic risk
group and site of birth, but who never showed signs of
diabetes-related autoimmunity or diabetes (the non-progressors).
Patterns of serum lipids, water soluble compounds and serum
albumin-bound metabolites differed in cord blood and samples
collected during infancy and early childhood, totally changing the
autoantibody-based prediction of type 1 diabetes. The identified
metabolites and extrapolated pathways imply that factors protecting
from oxidative stress and inflammation are highly important
inhibitors of disease progression, providing potential targets for
diabetes prevention.
[0046] Selection of Subjects
[0047] The DIPP project has been carried out in three cities in
Finland with a combined annual birth rate of 11,000, representing
almost 20% of births in Finland. The project was launched in Turku
in November 1994; Oulu joined the study one year and Tampere three
years later. HLA-DQB1 alleles *02, *0301, *0302, *0602 and *0603
were analyzed, and males positive for DQB1*02 were further typed
for DQA1 alleles *0201 and *05 in the Turku cohort. The defined PCR
amplified gene sequences were hybridized in solution with
allele-specific, lanthanide chelate labelled oligonucleotide
probes, and the hybridization products were detected using
time-resolved fluorimetry (Victor, Wallac, Turku). By Jun. 6, 2006,
107,484 consecutive newborns and their older siblings had been
screened, and around 8,000 children with genetic risk continued in
the follow-up.
[0048] Our attempts to include to the screening analysis of
polymorphisms of the insulin promoter region, CTLA4 and PTPN22 only
marginally improved screening efficacy with rather poor
cost-benefit ratio, forcing us to drop these assays from the
routine screening, although we have continued using these assays
for selected research purposes.
[0049] Of the study participants, 1445 had at least once been
positive for autoantibodies against islet cells, insulin, glutamic
acid decarboxylase or IA-2 protein. 516 of them had more than one
type of antibodies, strongly increasing their likelihood of
progressing to diabetes. Finally, 137 children developed overt type
1 during the follow-up (FIG. 1a-b). The majority of these 137
children developed as the first antibody IAA either alone or with
ICA or GADA, while IA-2A was usually a late-appearing antibody.
Some children developed diabetes rapidly within the first year of
age, while other children with closely similar autoantibody
patterns survived for years without progressing to overt diabetes
(see e.g. FIG. 1b). Autoantibody values commonly varied markedly
during the follow-up, but the values often slowly declined before
development of clinical diabetes (see e.g. FIG. 1b).
[0050] Longitudinal serum collection from birth until disease onset
(and possibly after) at 3-6 month intervals affords detailed study
of disease pathogenesis and potential early mechanisms. The
emergence of autoantibodies versus time is shown in FIG. 1b.
[0051] Subjects who progressed to overt Type 1 Diabetes were
selected from the DIPP trial, matched by HLA genotype, gender, city
and period of birth. Total 41 progressors and 54 non-progressors
were selected, accounting to 950 samples (FIG. 1c). For the
experiments and data analyses, the samples were further divided
into two separate batches based on city of birth: Turku (13
progressors, 26 non-progressors) and Oulu (28 progressors, 28
non-progressors).
[0052] To compare metabolomics findings in the genetically defined
DIPP population to a genetically non-defined group of children
having prospectively collected sample series available between the
ages of 7 months and post-puberty, we selected all six children who
in the Special Turku Coronary Risk Factor Intervention Project for
Children (STRIP) 8 had developed type 1 diabetes, and their six
age- and sex-matched healthy controls from the same study (total 89
samples). The STRIP study comprised at recruitment 1062 children,
out of whom over 700 continued in the study at child's age of 10.5
years. Only some of the children who developed diabetes carried HLA
risk alleles, but all had multiple autoantibodies before clinical
diabetes developed.
[0053] In our study of metabolomics data, we were particularly
interested in three types of comparisons in the context of Type 1
Diabetes (FIG. 1d): over-all differences in longitudinal profiles,
age-based comparison between progressors and non-progressors, and
metabolite profile changes related to associated with emergence of
autoimmunity.
[0054] Lipidomic Analysis Reveals Age as the Main Confounding
Factor
[0055] We performed lipidomics analysis on all selected 1039
samples using the UPLC-MS platform. While the data processing
generated large number of unidentified peaks, the data analysis was
limited to 186 lipid molecular species identified across all
batches. In order to explore the data structure and identify the
main confounding factors affecting the lipid profiles, we performed
the Sammon's non-linear mapping.sup.9, which maps the samples
nonlinearly from high (e.g. 186)-dimensional space to
low-dimensional space, while aiming to preserve profile (e.g.
Euclidean) distances between the samples. Compared to more commonly
utilized linear methods such as Principle Components
Analysis.sup.10, the Sammon's method is superior in ability to
extract information from highly interdependent features.sup.11 and
is a more direct way to visualize similarities of profiles from the
raw data.
[0056] The FIG. 2 displays the results of Sammon's mapping of the
Oulu DIPP batch for four potential confounding factors: individual
ID, gender, age, and sample age. It is evident that neither sample
age nor gender are major factors affecting similarities of lipid
profiles. However, the profiles do cluster on age (FIG. 2c), i.e.
the lipid profiles of children at early age are more similar to
each other than to their profiles at later stage. This can be
expected both due to diet, which varies with age and is generally
more uniform at early age, as well as due to significant changes of
childrens' metabolism due to their development. Interestingly,
between-individual differences can also be detected (FIG. 2d).
[0057] Early Age Serum Lipidome Differences Between Progressors and
Non-Progressors Prior to Autoimmunity
[0058] In order to examine the feasibility of early age disease
prediction, we performed multivariate cross-sectional analysis for
specific age groups using partial least squares discriminant
analysis.sup.12. The PLS/DA models were developed independently for
the three batches analyzed.
[0059] We found that there are clear differences between
progressors and non-progressors already at age of one year, and
that these differences are attributed to the same or related
molecular species across all three batches. We also applied the
model developed for DIPP Turku batch to select most important lipid
molecular species based on VIP analysis. A new PLS/DA model was
developed based on selected lipid species and applied to the other
two batches, and we found that it correctly predicts the onset of
diabetes.
[0060] Our results imply the lipidomics strategy, possibly in
combination with prior genetic screening, may markedly antedate and
improve accuracy of defining which children will later progress to
autoimmunity and over Type 1 Diabetes.
[0061] Consistent Differences in Plasmalogen Molecular Species
Between Cases and Controls
[0062] The early age differences found in serum lipid profiles
suggest disease-related events occur much earlier than previously
thought. In order to examine within-individual lipid level changes
in time and so determine consistency of observed changes in serum
lipidome, we studied the longitudinal profiles of each identified
lipid molecular species. Noticeably, we found decreased level of
multiple choline plasmalogen molecular species in children who
later progressed to autoimmunity and overt Type 1 Diabetes already
at an early age, i.e. markedly before development of signs of
autoimmunity (FIG. 3). The differences persist for all ages and the
plasmalogen levels do not appear to be affected by the emergence of
the disease itself (last time point for progressors).
[0063] Plasmalogens, a sub-class of ether linked phospholipids,
have been previously implicated in protection against oxidative
damage.sup.13-15. Reactive oxygen species (ROS) have been proposed
to play an important role in .beta.-cell destruction and it has
been shown that exposure of pancreatic islets to cytokines
increases ROS production and leads to oxidative damage to .beta.
cells.sup.16. The .beta. cells are particularly susceptible to
oxidative damage as they contain low levels of antioxidant
enzymes.sup.17.
[0064] Antioxidant therapies have been proposed as a possible
strategy to prevent diabetes.sup.18, but the results so far are
confusing.sup.19. Our results suggest that the ability to protect
against oxidative damage plays a major role in Type 1 Diabetes
pathogenesis, not the ROS generation itself.
[0065] It is known that the last parts of plasmalogen synthesis are
localized the endoplasmic reticulum (ER).sup.20. There is
substantial evidence from in vivo studies that ER stress plays an
important role in disease pathogenesis.
[0066] Cord Blood Analysis Reveals Decreased Phosphocholine Levels
in Children who Later Progressed to Diabetes
[0067] The early age differences in lipid phenotype raise the
possibility that the metabolic phenotypes of children who later
progressed to diabetes differ already at birth. For that purpose we
examined the cord blood samples of 39 children, of which 15 later
progressed to Type 1 diabetes until age 12 or earlier. The children
were born in Turku, but were not the same as studied in previous
analyses.
[0068] The multivariate analysis identified two major factors
affecting the grouping of samples (FIG. 4). The increased levels of
triacylglycerols affected both the progressors and non-progressors.
However, another major factor that appears to discriminate the
majority of the samples from the two groups is change in
phospholipids levels (FIGS. 4a and 4b). The plasmalogen species
GPCho(36:2e) found to be downregulated in progressors already at an
early age does not differ significantly between the groups (FIG.
4c). However, the total ester linked phosphocholine levels, the
most abundant phospholipids species in serum, are significantly
downregulated in progressors already at birth (FIG. 4d).
[0069] Seroconversion
[0070] We also investigated whether the observed lipid profile
changes associate with the emergence of autoimmunity. For that
purpose we compared serum lipid profiles within the period of 6
months prior to seroconversion and the period immediately following
the seroconversion.
[0071] The choline plasmalogen levels of progressors, as already
shown in FIG. 3, did not alter with emergence of autoimmunity. The
major factor prior to seroconversion in progressors is upregulated
lysophosphatidylcholine (FIG. 5). The lysophosphatidylcholine
(LysoPC) has been associated with inflammation.sup.21, therefore
suggesting an existence of an event leading to inflammation prior
to autoimmunity. Importantly, LysoPC has been shown to enhance
cytokine production.sup.22. The specific upregulation of LysoPC is
transient; it appears only within the short time interval.
[0072] The changes following the seroconversion are dominated by
increase in ethanolamine plasmalogen levels (FIG. 5). This suggests
the increase in these ether linked phospholipids is a normal
systemic response to increase in oxidative damage.
[0073] In summary, the longitudinal serum lipid profiles of
children who later developed Type 1 diabetes revealed several
successive events leading to autoimmunity and the disease (FIG. 6),
suggesting a key role of phospholipids metabolism in early disease
pathogenesis. The emerging picture of disease pathogenesis reveals
complex interplay of pro-pathogenic factors and the compensatory
responses.
[0074] Feasibility of Prediction of Type 1 Diabetes at an Early
Age
[0075] Lipidome changes observed suggest that disease prediction
using metabolic profiling prior to seroconversion may be feasible.
A classification algorithm was therefore developed based on the
extended lipid profiles from the randomly selected subset of 60% of
progressors and non-progressors. Based on known longitudinal
profile variation and no observed dependence on confounding
factors, only ether phospholipids were considered as potential
biomarkers. Best disease prediction was observed at an early age,
with the optimal biomarker at age 1.5 year (range 0.5-2.5 years)
consisting of GPCho(O-18:1/16:0) molecular species (Table 1). The
classification rule for progressors consisted of a requirement that
the lipid concentration lies below 4.09 .mu.mol/L.
[0076] The performance of the classifier was assessed by testing
the null hypothesis that the test outcome shows no association with
onset of type 1 diabetes. In order to control for bias, test and
training sets were randomly selected 1000 times. For each
selection, lipid-specific classification thresholds were determined
on the training set and the classification accuracy was assessed in
the test set. Binomial distributions were used to exactly calculate
the P-values corresponding to probabilities of obtaining at least
the observed number of true positives (TP) or at most the observed
number of false positives (FP) if the random classifier (TP=FP)
corresponding to the null hypothesis was used. Summary statistics,
median and 80% confidence interval of each variable are
reported.
TABLE-US-00001 TABLE 1 Performance of the classifier consisting of
a single ether phosphatidylcholine GPCho(O-18:1/16:0). A subject is
classified as a progressor if the ether phosphatidylcholine
concentration is below 4.1 .mu.mol/L, with 90% CI = [4.0 .mu.mol/L,
4.7 .mu.mol/L]. Autoantibody positive samples are excluded from the
analysis. Odds Sample series Age range TP/P P (TP) FP/N P (FP)
ratio Training set Average overall samples 12 [9, 14]/22 1.5
.times. 10.sup.-4 7 [5, 11]/40 4.2 .times. 10.sup.-6 12.2 [4.1,
46.9] per subject within 0.5-2.5 year age period Test set Average
overall samples 7 [3, 9]/14 0.051 6 [2, 12]/26 0.014 3.0 [1.1, 8.3]
per subject within 0.5-2.5 year age period Test set One sample per
subject, 7 [4, 10]/14 0.036 6 [3, 11]/26 0.006 3.2 [1.3, 8.3]
closest to age 1.5 year TP, number of true positives; P, number of
positives (i.e., progressors); P (TP), probability number of true
positives is greater than TP by chance; FP, number of false
positives; N, number of negatives (i.e., non-progressors); P (FP),
probability number of false positives is less than FP by chance.
90% confidence intervals for TP, FP, and Odds ratios, based on 1000
random selections of test and training sets, are shown in
brackets.
[0077] Methods
[0078] Serum collection. Vena blood samples were collected from
children during the years 1994-2004. The samples were taken various
times through the day without fasting. Blood samples were taken by
venous withdrawal using a needle and BD Vacutainer.RTM. Plastic
Tubes or Vacutainer.RTM. Plus Plastic Tubes. (BD Vacutainer.RTM.
SSTTM Tubes contain spray-coated silica and a polymer gel for serum
separation.) The tubes were left at RT 30-60 min to coagulate.
Serum was separated by centrifugation at 1300 rcf for 10 min at
room temperature. The serum samples were stored in small plastic
tubes at -80.degree. C.
[0079] Lipidomics. An aliquot (10 .mu.l) of an internal standard
mixture containing 11 lipid classes, and 0.05M sodium chloride (10
.mu.l) was added to serum samples (10 .mu.l) and the lipids were
extracted with chloroform/methanol (2:1, 100 .mu.l). After
vortexing (2 min), standing (1 hour) and centrifugation (10000 RPM,
3 min) the lower layer was separated and a standard mixture
containing 3 labelled standard lipids was added (10 .mu.l) to the
extracts. The internal standard mixture contained the following
lipid compounds (pg/ml) with heptadecanoic acid (C17:0) as the
esterified fatty acid:
[0080] D-erythro-Sphingosine-1-Phosphate (9.3 .mu.g/ml; C17 Base,
Avanti Polar Lipids),
[0081] 1-Heptadecanoyl-2-Hydroxy-sn-Glycero-3-Phosphocholine (8.8
.mu.g/ml; Avanti Polar Lipids),
[0082] 1-Monoheptadecanoin (rac) (9.3 .mu.g/ml; Larodan Fine
Chemicals),
[0083] 1,2-Diheptadecanoyl-sn-Glycero-3-[Phospho-rac-(1-glycerol)]
(9.6 .mu.g/ml; Avanti Polar Lipids),
[0084] N-Heptadecanoyl-D-erythro-Sphingosine (9.2 .mu.g/ml; Avanti
Polar Lipids),
[0085] 1,2-Diheptadecanoyl-sn-Glycero-3-[Phospho-L-Serine] (8.6
.mu.g/ml; Avanti Polar Lipids),
[0086] 1,2-Diheptadecanoyl-sn-Glycero-3-Phosphocholine (9.9
.mu.g/ml; Avanti Polar Lipids),
[0087] 1,2-Diheptadecanoyl-sn-Glycero-3-Phosphate (8.5 .mu.g/ml;
Avanti Polar Lipids),
[0088] 1,2-Diheptadecanoyl-sn-Glycero-3-Phosphoethanolamine (8.9
.mu.g/ml; Avanti Polar Lipids),
[0089] 1,2-Diheptadecanoin (rac) (10.2 .mu.g/ml; Larodan Fine
Chemicals) and
[0090] Triheptadecanoin (10.4 .mu.g/ml; Larodan Fine
Chemicals).
[0091] The labeled standard mixture consisted of
[0092] L-.alpha.-Lysophosphatidylcholine-Palmitoyl-D3 (9.3
.mu.g/ml; Larodan Fine Chemicals),
[0093] 1,2-Dipalmitoyl-D6-sn-Glycerophosphatidylcholine (11.7
.mu.g/ml; Larodan Fine Chemicals) and
[0094] Tripalmitin-1,1,1-.sup.13C3 (10.0 .mu.g/ml; Larodan Fine
Chemicals).
[0095] When analysing the first 232 samples (Batch 1), only one
standard mixture (25 .mu.l) containing triheptadecanoin (0.804
mg/ml; Larodan Fine Chemicals) and
1,2-dipentadecanoyl-sn-glycero-3-phosphocholine (0.304 mg/ml;
Larodan Fine Chemicals) was added to serum samples (15 .mu.l)
before lipid extraction with chloroform/methanol (2:1, 100
.mu.l).
[0096] Lipid extracts were analysed on a Waters Q-Tof Premier mass
spectrometer combined with an Acquity Ultra Performance LC.TM.
(UPLC). The column, which was kept at 50.degree. C., was an Acquity
UPLC.TM. BEH C18 10.times.50 mm with 1.7 .mu.m particles. The
binary solvent system included A. water (1% 1M NH.sub.4Ac, 0.1%
HCOOH) and B. LC/MS grade (Rathburn) acetonitrile/isopropanol (5:2,
1% 1M NH.sub.4Ac, 0.1% HCOOH). The gradient started from 65% A/35%
B, reached 100% B in 6 min and remained there for the next 7 min.
The total run time including a 5 min re-equilibration step was 18
min. The flow rate was 0.200 ml/min and the injected amount 0.75
.mu.l. The temperature of the sample organizer was set at
10.degree. C. The lipid profiling was carried out on Waters Q-Tof
Premier mass spectrometer using ESI+ mode. The data was collected
at mass range of m/z 300-1200 with a scan duration of 0.2 sec. For
the last samples the scan time was changed to 0.02 sec. The source
temperature was set at 120.degree. C. and nitrogen was used as
desolvation gas (800 L/h) at 250.degree. C. The voltages of the
sampling cone and capillary were 39 V and 3.2 kV, respectively.
Reserpine (50 .mu.g/L) was used as the lock spray reference
compound (5 .mu.l/min; 10 sec scan frequency). Tandem mass
spectrometry was used for the identification of selected molecular
species of lipids. MS/MS runs were performed by using ESI+ mode,
collision energy ramp from 15 to 30 V and mass range starting from
m/z 150. The other conditions were as shown above.
[0097] Processing and analysis of metabolomics data. Data was
processed using MZmine software version 0.60.sup.23,24. Metabolites
were identified using internal spectral library.
[0098] Partial least squares discriminant analysis
(PLS/DA).sup.12,25 was utilized as a supervised modeling method
using SIMPLS algorithm to calculate the model.sup.26. Venetian
blinds cross-validation method.sup.27 and Q.sup.2 scores were used
to develop the models. Top loadings for latent variables associated
with drug specific effects were reported. The VIP (variable
importance in the projection) values.sup.28 were calculated to
identify the most important molecular species for the clustering of
specific groups. Multivariate analyses were performed using Matlab
version 7.2 (Mathworks, Inc.) and the PLS Toolbox version 4.0
Matlab package (Eigenvector Research, Inc.).
[0099] Other Serum Metabolites (i.e. Non-Phospholipids) Found in
Cord Blood
[0100] Methods:
[0101] The serum samples were prepared as follows: 400 .mu.l
methanol and 10 .mu.l 250 ppm d3-palmitic acid (internal standard)
were added to a 25 .mu.l serum sample. The samples were vortexed
for 30 seconds. After 30 minutes the samples were centrifuged for 3
min at 10000 rpm. Supernatant was moved to a GC vial and evaporated
to dryness under nitrogen. The samples were silylated with 20 .mu.l
MOX (45.degree. C., 60 min) and 20 .mu.l MSTFA (45.degree. C., 60
min). 5 .mu.l of retention index solution was added to samples (600
ppm C11, C15, C17, C21 and C25 alkanes).
[0102] Instrument:
[0103] The instrument used was a Leco Pegasus 4D GCxGC-TOF mass
spectrometer with Agilent 6890N GC and Combi PAL autosampler. The
instrument parameters were as follows:
[0104] 2 .mu.l split injection 1:20 for serum samples.
[0105] First column: RTX-5, 10 m.times.180 .mu.m.times.0.20
.mu.m
[0106] Second column: BPX-50, 1.10 m.times.100 .mu.m.times.0.10
.mu.m
[0107] Helium 35.33 psig constant pressure
[0108] Temperature Programmes:
[0109] Primary oven: Initial 50.degree. C., 1
min..fwdarw.280.degree. C., 7.degree. C./min, 5 min.
[0110] Secondary oven: +10.degree. C. above primary oven
temperature.
[0111] Second dimension separation time 4 s.
[0112] MS measurement 40-700 amu, 100 spectra/s.
[0113] Method Characteristics:
[0114] The performance characteristics of GCxGC-TOF have been
tested with three pure, non-extracted, reference compounds. All
compounds were made in eight concentration levels between 10 and
30000 ng/sample.
[0115] L-Threonine:
[0116] Linear range: 7.4-2200 ng
[0117] Correlation coefficient (at linear range): 0.99975
[0118] Relative standard deviation (8 samples, 7440 ng): 7.60%
[0119] S/N at lowest concentration 7.4 ng: 56.6
[0120] Lauric Acid:
[0121] Linear range: 10-30000 ng
[0122] Correlation coefficient: 0.99737
[0123] Relative standard deviation (7 samples, 10100 ng): 2.61%
[0124] S/N at lowest concentration 10.1 ng: 115.3
[0125] Cholesterol:
[0126] Linear range: 10-30000 ng
[0127] Correlation coefficient: 0.99999
[0128] Relative standard deviation (7 samples, 10000 ng): 2.89%
[0129] S/N at lowest concentration 10.0 ng: 62.7
[0130] Data Processing:
[0131] ChromaTof software was used for within-sample data
processing, and in house made software was used for alignment and
peak matching across samples. The peaks were filter based on number
of detected peaks in the total profile of 36 samples (set to 12
peaks found minimum) and based on identity match in the database
(similarity index threshold=800).
[0132] Results: The results are shown in Table 2 below. The column
Fold (median) shows the ratio of median value of metabolite levels
of children who progressed to type 1 diabetes and median value for
children who remained autoantibody negative during the follow-up
(non-progressors). p(Wilcoxon) is the p value based on Wilcoxon
rank sum test comparing the two groups. The column Fold (mean)
shows the ratio of mean value of metabolite levels of children who
progressed to type 1 diabetes and mean value for children who
remained autoantibody negative during the follow-up
(non-progressors). p(ttest) is the p value based on two-sided
t-test comparing the two groups.
[0133] Table 2 (will be added in landscape format)
TABLE-US-00002 TABLE 2 Name Fold (median) p(Wilcoxon) Fold (mean)
p(ttest) Tryptophan, bis(trimethylsilyl)- 0.68 0.0224 0.52 0.0197
Ribitol, 1,2,3,4,5-pentakis-O-(trimethylsilyl)- 0.45 0.0369 0.42
0.0256 Pentanedioic acid, 2-[(trimethylsilyl)oxy]-,
bis(trimethylsilyl) ester 0.36 0.0205 0.57 0.0258 Glycine,
N,N-bis(trimethylsilyl)-, trimethylsilyl ester 0.86 0.0694 0.72
0.0272 Eicosanoic acid, trimethylsilyl ester Inf 0.0267 0.0296
1,2,3-Propanetricarboxylic acid, 2-[(trimethylsilyl)oxy]-,
tris(trimethylsilyl) 0.60 0.0184 0.70 0.0298 MYRISTOLEIC ACID 1TMS
0.00 0.0587 0.30 0.0330 MANNITOL TMS 0.45 0.0248 0.39 0.0332
Creatinine enol N1,N3,O-tris(trimethylsilyl) 0.82 0.0923 0.77
0.0476 Butanedioic acid, bis(trimethylsilyl) ester 0.24 0.0419 0.46
0.0494 HEPTANOIC ACID TMS 1.19 0.0390 0.1719 2-KETOGLUTARIC
ACID-METHOXIME-MONOTMS 0.48 0.0314 0.66 0.2604 indicates data
missing or illegible when filed
[0134] It will be appreciated that the methods of the present
invention can be incorporated in the form of a variety of
embodiments, only a few of which are disclosed herein. It will be
apparent for the expert skilled in the field that other embodiments
exist and do not depart from the spirit of the invention. Thus, the
described embodiments are illustrative and should not be construed
as restrictive.
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